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About This Role
Job Description Role: Manager Analytics Automation \& AI (AAA)
Location: Atlanta, Georgia (Remote)
Job Type: Contract (12\+ Months)
Position Overview
Client is seeking a highly specialized, technically grounded, and business\-focused Manager of Analytics Automation \& AI (AAA) to spearhead our next\-generation operational intelligence and systemic evolution initiatives. This role requires a distinct, hybrid professional paradigm—blending deep, enterprise\-grade Robotic Process Automation (RPA) design patterns with modern Cloud AI ecosystems, Salesforce omni\-channel structures, and formal financial service architecture.
You will not simply document parameters; you will serve as the chief architecture liaison and strategist who scales intelligent agent boundaries across core banking grids, CRM engines, and multi\-tenant cloud platforms. If you hold deep expertise across PSM frameworks, legacy modernization, and high\-velocity analytics engineering, this position offers an impactful playground to transform complex operational frameworks.
Qualifications \& Requirements
Basic Qualifications:
- Experience: Minimum of 10 years of direct experience executing Business Analysis, System Integration, and Product Customization for enterprise IT architectures.
- Domain Expertise: 5\+ years of deep domain experience designing and maintaining end\-to\-end RPA platform solutions and multi\-cloud CRM (Salesforce) integrations.
- Methodology \& Alignment: Proven mastery across diverse SDLC paradigms (Agile, Scrum, and Waterfall frameworks) utilizing Jira to maintain complete pipeline transparency.
Technical Competencies \& Niche Combinations
- Platform Mastery: Advanced configuration design in enterprise RPA platforms (UiPath and Blue Prism) combined with modern API\-driven application landscapes.
- AI \& Advanced Cloud Analytics: Hands\-on structural exposure to Google Cloud AI Agents, Tableau visualization architectures, and extensive data layer manipulation.
- Engineering Rigor: Advanced query optimization engineering (SQL query tuning, index balancing) to drive significant performance upgrades across relational backends.
- Testing \& Integrity: Firm understanding of structural defect management workflows, automated testing frameworks, and formal validation testing protocols (ISTQB criteria).
Key Responsibilities
- Intelligent AI Automation Strategy: Architect, execute, and scale an enterprise end\-to\-end RPA roadmap. Mature targeted automation concepts into fully scaled agentic workflows by leveraging Google Cloud AI Agents integrated with core delivery models.
- Salesforce \& Omni\-Channel Orchestration: Lead complex integration ecosystems combining Salesforce Service Cloud, Marketing Cloud, and Social Listening platforms to achieve unified consumer operational flows.
- Advanced Requirements Management \& Design: Author robust, bulletproof Business, Technical, and Product Requirement Documents (BRD, FRD, PDD, SDD). Bridge gaps between senior executive management, core architects, and quality engineering teams by translating large\-scale strategic epics into micro\-executable Agile user stories.
- Architectural Risk Mitigation: Perform deep data lineage analysis, structural validation, and risk mitigation across complex system interfaces to prevent downstream defect propagation during high\-velocity deployments.
- Agile \& Scrum Governance: Facilitate daily Scrums, user story grooming, and sprint reviews. Standardize tracking frameworks using Jira Agile dashboards, burn\-up/burn\-down metrics, and centralized SharePoint configurations.
- Mentorship \& Continuous Engineering: Mentor upcoming technology and analytics professionals. Conduct systematic knowledge\-sharing sessions while running infrastructure cost\-benefit and performance evaluations to identify ongoing efficiency opportunities.
Note
Preferred Qualifications \& Certifications:
Certifications: Professional Scrum Master I (PSM I), ISTQB Foundation Level, and specialized accreditations in Tableau Data Visualization / Cloud AI engineering.
Pre\-Screening Questions
Q1: Do you have 5\+ years of hands\-on experience designing and implementing end\-to\-end RPA solutions using UiPath and/or Blue Prism?
Q2: Do you have hands\-on experience integrating Google Cloud AI Agents or cloud\-based AI solutions with Salesforce Service Cloud, Marketing Cloud, and/or Social Listening platforms?
Q3: Do you have advanced hands\-on experience with SQL query optimization, query tuning, and database index balancing to improve application or infrastructure performance?
Q4: Do you have experience creating and managing technical solution documentation, including BRDs, FRDs, PDDs, and SDDs, while working within Agile/Scrum environments using Jira?
Additional Information
All your information will be kept confidential according to EEO guidelines.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tms Llc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Tms Llc AI Hiring
Tms Llc has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Myrtle Point, OR, US, Tampa, FL, US, Campbell, CA, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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